CCL18, CHI3L1, ANG2, IL-6 systemic levels are associated with the extent of lung damage and radiomic features in SARS-CoV-2 infection.

OBJECTIVE AND DESIGN: We aimed to identify cytokines whose concentrations are related to lung damage, radiomic features, and clinical outcomes in COVID-19 patients. MATERIAL OR SUBJECTS: Two hundred twenty-six patients with SARS-CoV-2 infection and chest computed tomography (CT) images were enrolled. METHODS: CCL18, CHI3L1/YKL-40, GAL3, ANG2, IP-10, IL-10, TNFα, IL-6, soluble gp130, soluble IL-6R were quantified in plasma samples using Luminex assays. The Mann-Whitney U test, the Kruskal-Wallis test, correlation and regression analyses were performed. Mediation analyses were used to investigate the possible causal relationships between cytokines, lung damage, and outcomes. AVIEW lung cancer screening software, pyradiomics, and XGBoost classifier were used for radiomic feature analyses. RESULTS: CCL18, CHI3L1, and ANG2 systemic levels mainly reflected the extent of lung injury. Increased levels of every cytokine, but particularly of IL-6, were associated with the three outcomes: hospitalization, mechanical ventilation, and death. Soluble IL-6R showed a slight protective effect on death. The effect of age on COVID-19 outcomes was partially mediated by cytokine levels, while CT scores considerably mediated the effect of cytokine levels on outcomes. Radiomic-feature-based models confirmed the association between lung imaging characteristics and CCL18 and CHI3L1. CONCLUSION: Data suggest a causal link between cytokines (risk factor), lung damage (mediator), and COVID-19 outcomes.

Impact of Preprocessing Parameters in Medical Imaging-Based Radiomic Studies: A Systematic Review.

BACKGROUND: Lately, radiomic studies featuring the development of a signature to use in prediction models in diagnosis or prognosis outcomes have been increasingly published. While the results are shown to be promising, these studies still have many pitfalls and limitations. One of the main issues of these studies is that radiomic features depend on how the images are preprocessed before their computation. Since, in widely known and used software for radiomic features calculation, it is possible to set these preprocessing parameters before the calculation of the radiomic feature, there are ongoing studies assessing the stability and repeatability of radiomic features to find the most suitable preprocessing parameters for every used imaging modality. MATERIALS AND METHODS: We performed a comprehensive literature search using four electronic databases: PubMed, Cochrane Library, Embase, and Scopus. Mesh terms and free text were modeled in search strategies for databases. The inclusion criteria were studies where preprocessing parameters' influence on feature values and model predictions was addressed. Records lacking information on image acquisition parameters were excluded, and any eligible studies with full-text versions were included in the review process, while conference proceedings and monographs were disregarded. We used the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) tool to investigate the risk of bias. We synthesized our data in a table divided by the imaging modalities subgroups. RESULTS: After applying the inclusion and exclusion criteria, we selected 43 works. This review examines the impact of preprocessing parameters on the reproducibility and reliability of radiomic features extracted from multimodality imaging (CT, MRI, CBCT, and PET/CT). Standardized preprocessing is crucial for consistent radiomic feature extraction. Key preprocessing steps include voxel resampling, normalization, and discretization, which influence feature robustness and reproducibility. In total, 44% of the included works studied the effects of an isotropic voxel resampling, and most studies opted to employ a discretization strategy. From 2021, several studies started selecting the best set of preprocessing parameters based on models' best performance. As for comparison metrics, ICC was the most used in MRI studies in 58% of the screened works. CONCLUSIONS: From our work, we highlighted the need to harmonize the use of preprocessing parameters and their values, especially in light of future studies of prospective studies, which are still lacking in the current literature.

Physical and dosimetric characterisation of different Contrast-Enhanced digital mammographic systems: A multicentric study.

PURPOSE: Contrast-enhanced digital mammography (CEDM) is a relatively new imaging technique recombining low- and high-energy mammograms to emphasise iodine contrast. This work aims to perform a multicentric physical and dosimetric characterisation of four state-of-the-art CEDM systems. METHODS: We evaluated tube output, half-value-layer (HVL) for low- and high-energy and average glandular dose (AGD) in a wide range of equivalent breast thicknesses. CIRS phantom 022 was used to estimate the overall performance of a CEDM examination in the subtracted image in terms of the iodine difference signal (S). To calculate dosimetric impact of CEDM examination, we collected 4542 acquisitions on patients. RESULTS: Even if CEDM acquisition strategies differ, all the systems presented a linear behaviour between S and iodine concentration. The curve fit slopes expressed in PV/mg/cm2 were in the range [92-97] for Fujifilm, [31-32] for GE Healthcare, [35-36] for Hologic, and [114-130] for IMS. Dosimetric data from patients were matched with AGD values calculated using equivalent PMMA thicknesses. Fujifilm exhibited the lowest values, while GE Healthcare showed the highest. CONCLUSION: The subtracted image showed the ability of all the systems to give important information about the linearity of the signal with the iodine concentrations. All the patient-collected doses were under the AGD EUREF 2D Acceptable limit, except for patients with thicknesses ≤35 mm belonging to GE Healthcare and Hologic, which were slightly over. This work demonstrates the importance of testing each CEDM system to know how it performs regarding dose and the relationship between PV and iodine concentration.

Safety injections of nuclear medicine radiotracers: towards a new modality for a real-time detection of extravasation events and 18F-FDG SUV data correction.

BACKGROUND: 18F-FDG PET/CT imaging allows to study oncological patients and their relative diagnosis through the standardised uptake value (SUV) evaluation. During radiopharmaceutical injection, an extravasation event may occur, making the SUV value less accurate and possibly leading to severe tissue damage. The study aimed to propose a new technique to monitor and manage these events, to provide an early evaluation and correction to the estimated SUV value through a SUV correction coefficient. METHODS: A cohort of 70 patients undergoing 18F- FDG PET/CT examinations was enrolled. Two portable detectors were secured on the patients' arms. The dose-rate (DR) time curves on the injected DRin and contralateral DRcon arm were acquired during the first 10 min of injection. Such data were processed to calculate the parameters ΔpinNOR = (DRinmax- DRinmean)/DRinmax and ΔRt = (DRin(t) - DRcon(t)), where DRinmax is the maximum DR value, DRinmean is the average DR value in the injected arm. OLINDA software allowed dosimetric estimation of the dose in the extravasation region. The estimated residual activity in the extravasation site allowed the evaluation of the SUV's correction value and to define an SUV correction coefficient. RESULTS: Four cases of extravasations were identified for which ΔRt [(390 ± 26) µSv/h], while ΔRt [(150 ± 22) µSv/h] for abnormal and ΔRt [(24 ± 11) µSv/h] for normal cases. The ΔpinNOR showed an average value of (0.44 ± 0.05) for extravasation cases and an average value of (0.91 ± 0.06) and (0.77 ± 0.23) in normal and abnormal classes, respectively. The percentage of SUV reduction (SUV%CR) ranges between 0.3% and 6%. The calculated self-tissue dose values range from 0.027 to 0.573 Gy, according to the segmentation modality. A similar correlation between the inverse of ΔpinNOR and the normalised ΔRt with the SUV correction coefficient was found. CONCLUSIONS: The proposed metrics allowed to characterised the extravasation events in the first few minutes after the injection, providing an early SUV correction when necessary. We also assume that the characterisation of the DR-time curve of the injection arm is sufficient for the detection of extravasation events. Further validation of these hypotheses and key metrics is recommended in larger cohorts.

Artificial Intelligence-Assisted Processing of Anterior Segment OCT Images in the Diagnosis of Vitreoretinal Lymphoma.

Anterior segment optical coherence tomography (AS-OCT) allows the explore not only the anterior chamber but also the front part of the vitreous cavity. Our cross-sectional single-centre study investigated whether AS-OCT can distinguish between vitreous involvement due to vitreoretinal lymphoma (VRL) and vitritis in uveitis. We studied AS-OCT images from 28 patients (11 with biopsy-proven VRL and 17 with differential diagnosis uveitis) using publicly available radiomics software written in MATLAB. Patients were divided into two balanced groups: training and testing. Overall, 3260/3705 (88%) AS-OCT images met our defined quality criteria, making them eligible for analysis. We studied five different sets of grey-level samplings (16, 32, 64, 128, and 256 levels), finding that 128 grey levels performed the best. We selected the five most effective radiomic features ranked by the ability to predict the class (VRL or uveitis). We built a classification model using the xgboost python function; through our model, 87% of eyes were correctly diagnosed as VRL or uveitis, regardless of exam technique or lens status. Areas under the receiver operating characteristic curves (AUC) in the 128 grey-level model were 0.95 [CI 0.94, 0.96] and 0.84 for training and testing datasets, respectively. This preliminary retrospective study highlights how AS-OCT can support ophthalmologists when there is clinical suspicion of VRL.